Detection of Grinding Temperatures Using Laser Irradiation and Acoustic Emission Sensing Technique

Abstract

Dr Xun Chen is a reader in Precision Engineering at the School of Computing and Engineering, University of Huddersfield, UK. He is also a founding member of the International Committee for Abrasive Technology. Dr Chen specialises in advanced manufacturing technology including application of computing science and artificial intelligence to manufacturing process monitoring and control, particularly to high efficiency precision grinding technology. His research work has been supported extensively by the Engineering and Physical Sciences Research Council and by industries. He has published more than 150 research papers widely in top journals in Mechanical Manufacturing Engineering. Dr. Chen delivers a number of lectures that covers mechanical manufacturing engineering and management as well as the application of computers. 2 Detection of grinding temperatures using laser irradiation and acoustic emission sensing technique. This paper presents a new method for the detection of grinding thermal behaviours using a laser irradiation technique. Laser irradiation was initially undertaken in the Lumonics JK704 Nd: YAG laser machine under mimic grinding conditions. Temperature elevation was controlled using laser irradiation by varying the laser energy and laser irradiation time. The signatures of acoustic emission (AE) were recorded as pure thermally induced AE signals. A series of grinding experiments were conducted separately to identify different AE sources during grinding. An artificial neural network (ANN) had been trained to distinguish high and low temperatures using laser thermal AE data. This trained ANN was then used to classify burn and no burn in the grinding zone. The classification accuracy achieved 71% when grinding Inconel718 materials. The novelty of this work is reflected in that the laser irradiation induced thermal AE signals can represent grinding thermal behaviour and can be used for grinding burn detection.

Guest Editorial: The 36th MATADOR Conference 2010

“…Another traditional process examined in two papers is grinding and here as well modern techniques are used. In one paper, the authors develop an artificial neural network model of grinding burn based on acoustic emission (AE) signals generated via laser irradiation [2]. In the other, use of minimum quantity lubrication (MQL) is studied [1].…”

Grinding Burn Detection Based on Cross Wavelet and Wavelet Coherence Analysis by Acoustic Emission Signal

“…Researchers simulated the grinding burn process by laser, from which pure AE burn signals were obtained. The results were used as reference to identify grinding burn [20][21][22]. Wu et al [23] carried out a series of grinding experiments to monitor grinding burn by AE sensor, dynamometer and surface roughness [23].…”

“…have been calculated and compared [7]. Then time-frequency methods such as Short Time Fourier Transform (STFT) [22], Wavelet Packet Transform (WPT) [20], Hilbert Huang Transform (HHT) [24] and Ensemble Empirical Mode Decomposition (EEMD) [21] have been introduced to analyze grinding burn signals.…”